STTR Phase II: TerraSentia: Ultra-compact, Autonomous, Teachable Under-canopy Phenotyping Robot for Plant Breeders and Crop Scientists

STTR 第二阶段：TerraSentia：面向植物育种者和作物科学家的超紧凑、自主、可教学的树冠下表型机器人

• 批准号: 1951250
• 负责人: Chinmay Soman
• 金额: $ 75万
• 依托单位: EarthSense, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951250&HistoricalAwards=false
• 关键词: STTR; Phase; II; TerraSentia; Ultra

The broader impact of this Small Business Technology Transfer (STTR) Phase II project include improving food security, while at the same time enhancing the economic viability and environmental sustainability of large-scale production agriculture. In order to improve crop varieties, agricultural production, and sustainability of farming, there is an urgent need for better technologies to acquire under-canopy plant trait and health data. Examples of high-value under-canopy data include emergence, stem width, corn ear height, plant life-cycle events like flowering and fruiting, and symptoms of pathogens, diseases, and nutrient deficiency. Because these data cannot be obtained by aerial imaging, under-canopy data collection has dramatically greater actionability and value compared to aerial data. However no cost-effective, scalable ways of collecting this data are currently available. In fact, the state of the art is manual data collection by crop scientists (and their students or interns), agronomists, crop-scouts or farmers - an extremely labor intensive, and therefore expensive way of collecting this highly valuable data. Our work will greatly enhance the availability of under-canopy data from field crops, benefiting crop scientists and agricultural product development professionals as well as enable large scale field monitoring and management in production agriculture. The commercial value of the field data for crop breeding is in excess of $50 Million/year for breeding major row-crops in the US.This STTR Phase II project proposes to establish autonomous data collection under-canopy from field crops using a low-cost ground robot. The proposed work will enhance the ability to collect data autonomously in full-scale crop-breeding fields throughout the season and enable on-site data analytics for remote sites with limited connectivity. Long-term field adaptive autonomy will be achieved through implementation of multiple low-cost sensors. Robot's real-time control algorithms will be developed to adapt camera perspective and robot path in order to obtain the highest quality information from the complex and dynamic under-canopy field environments. Finally, the research will develop hardware specific edge-compute versions of the analytics algorithms to enable on-site data analysis. These innovations will together enable global deployment of the system for effective data collection and phenotyping.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这一小企业技术转让第二阶段项目的更广泛影响包括改善粮食安全，同时提高大规模生产农业的经济可行性和环境可持续性。为了改善作物品种、农业生产和耕作的可持续性，迫切需要更好的技术来获取冠下植物性状和健康数据。高价值的冠下数据包括出苗、茎宽、玉米穗高、植物生命周期事件（如开花和结果）以及病原体、疾病和营养缺乏的症状。由于这些数据无法通过航空成像获得，因此与航空数据相比，冠层下数据收集具有更大的可操作性和价值。然而，目前还没有成本效益高、可扩展的方法来收集这些数据。事实上，最先进的技术是由作物科学家（及其学生或实习生）、农学家、作物侦察员或农民手动收集数据-这是一种劳动密集型的，因此昂贵的收集这种极有价值的数据的方式。我们的工作将大大提高田间作物冠层下数据的可用性，使作物科学家和农产品开发专业人员受益，并使生产农业中的大规模田间监测和管理成为可能。 在美国，用于作物育种的田间数据的商业价值超过5000万美元/年，用于培育主要行栽作物。该STTR第二阶段项目建议使用低成本地面机器人建立田间作物冠下的自主数据收集。拟议的工作将提高整个季节在全规模作物育种领域自主收集数据的能力，并为连接有限的远程站点提供现场数据分析。通过实施多个低成本传感器，将实现长期的现场自适应自治。机器人的实时控制算法将被开发，以适应相机的角度和机器人的路径，以获得最高质量的信息，从复杂和动态的冠下领域的环境。最后，该研究将开发分析算法的硬件特定边缘计算版本，以实现现场数据分析。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。